Document-topic probabilities using LDA.

<p>Quantitative representation of how each document in corpus relates to identified topic. (See Fig 4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0313259#pone.0313259.s001" target="_blank">S1 Appendix</a> for an alternative visualiza...

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Bibliographic Details
Main Author:	Julia C. Lensing (20521847) (author)
Other Authors:	John Y. Choe (20390917) (author), Branden B. Johnson (6312122) (author), Jingwen Wang (219000) (author)
Published:	2025
Subjects:	Biotechnology Cancer Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified published daily worldwide made publicly available level emergency management gpt ), offer disrupt lifeline infrastructures survey results highlight known risk associated allowing future generations reveals potential differences potential user groups div >< p practical disaster reports case study illustrates practical reports future disasters case study brief survey vital resources various forms trained transformer text mining supporting data response plans resiliency plans reports serve primarily interpretable practice due past events pacific northwest oregon state limited impact impact assessments generative pre explore opportunities experienced professionals ensure reproducibility challenges associated better mitigate action reports
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Document-topic probabilities using LDA.

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